Introduction of SVM Learning in Strategies

This post is derived from a comment I received my last post on probability density function for an adaptive RSI strategy. pinner made the following observation:

“Alternatively you could regress the returns against the set of 6mo & 1yr RSI points as a means to determine the best decision. While this approach probably requires more historical data, it also affords more detailed metrics from which to make each decision.”

I am personally not a big fan of simple regression model for my trading decisions. However, I think there is something in the concept. Such a setup is also a good place to use a common traditional machine learning technique; the support vector machine (SVM).

From wikipedia: given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. Intuitively, an SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.

In concrete terms, the training algorithm separate the data into up days and down days then look at the predictors value. It then creates a set of rules dividing the data; these rules minimize the classification error while also maximizing the margin of safety, thus giving the model more room, resulting (hopefully) in a greater accuracy. Based on this set of rules, the algorithm classifies new data in a category. Note that this is not a numeric prediction (i.e. next day return should be xx%) it is a binary factor prediction, thus allowing us to derive a probability along with the prediction. It comes in handy when we want a confidence based system.

This is a nice technique, but it has its drawbacks. First of all, it is a parameter dependant technique. The effectiveness of the svm is mostly determined by two parameters. It is usually recommended to test different values for the pair and to retain the pair that performs best during cross-validation for the model. This can become quite computationally annoying. Without getting to technical (or into details), if we want a non-linear classification algorithm, we have to choose the type of kernel function we want; there is several.

I just wanted to throw the idea out there, since pinner’s suggestion was a good one. If readers are interested to try it out, I encourage you to send me the results, I would be happy to post them. Alternatively, I might post some results depending on the interest. I just wanted to introduce support vector machine and its potential application when developing strategies.

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4 thoughts on “Introduction of SVM Learning in Strategies”

hi, nice post– SVM is quite powerful but highly dependent on what situations you use to train it to be successful. furthermore the normalization and data pre-processing is also an important step. like neural networks, SVM faces difficulties in accurately classifying the middle of the distribution (say the 30th to 70th percentiles)–unlike neural networks it does generalize much better.SVM seems to exceed at identifying the really high edge situations much better than other methods–especially with PKR.
it is true that SVM has so many possible methods of application it would be unfair to paint them all with the same brush. nonetheless practioners will have a much easier time achieving successful results applying SVM than most other AI types.
best
david varadi

Hi David,
I agree with everything. SVMs, along with other traditional machine learning methods, are surely not the end all be all method to generate alpha. Actually I find difficult to generate consistent excess return using classic machine learning algorithms. But as you say, SVMs performed better than neural networks or random forest when I played around with them. Also, like with every machine learning system, it is very predictor dependent (i.e. GIGO).

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The content of this blog is provided for informational purposes only. Nothing herein should ever be considered investment advice or recommendation to buy, sell or hold any securities. Simulated past performance of a trading strategies does not necessarily accurately represents future performance. All information herein is not guaranteed to be error free. All trading decisions you make should be your own based on your own analysis and you should accept full responsibility for the profits/losses incurred during actual trading.